scholarly journals Maximum Likelihood Position Location with a Limited Number of

2011 ◽  
Vol 9 (01) ◽  
Author(s):  
D. Munoz-Rodriguez ◽  
L. Suarez-Robles ◽  
C. Vargas-Rosales ◽  
J. R. Rodriguez-Cruz
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Root Mean Square ◽  
Likelihood Estimation ◽  
Error Distribution ◽  
Location Information ◽  
Mean Square ◽  
Position Location ◽  
Rms Error ◽  
Simulation Results

A Position Location (PL) scheme for mobile users on the outskirts of coverage areas is presented. The proposed methodology makes it possible to obtain location information with only two land-fixed references. We introduce a¨general formulation and show that maximum-likelihood estimation can provide adequate PL information in this scenario. The Root Mean Square (RMS) error and error-distribution characterization are obtained for different propagation scenarios. In addition, simulation results and comparisons to another method are provided showing the accuracy and the robustness of the method proposed. We study accuracy limits of the proposed methodology for different propagation environments and show that even in the case of mismatch in the error variances, good PL estimation is feasible.

Study on Optimization of the Number of DF Stations

2014 ◽  
Vol 709 ◽  
pp. 485-490
Author(s):  
Xiang Wu ◽  
Jun Jun Zong ◽  
Xun Xue Cui ◽  
Chuan Xu Liu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Direction Finding ◽  
Location Error ◽  
Location Accuracy ◽  
Error Models ◽  
Reasonable Number ◽  
Simulation Results

Reasonable number of direction finding station is examined in multi-station bearing-crossing location. Though it is believed that increasing the number of station is helpful to improve the location accuracy, In the paper, the maximum likelihood estimation (MLE) as an example. The algorithms and the location error models are given. The simulation results show that the location accuracy will be improved quickly with the increase of the number of the measuring participants, but the improvement will be sharply slowed down if too many station involved, which also boost the complexity of location.

The Pricing Problem

1987 ◽  
Vol 1 (3) ◽  
pp. 349-366
Author(s):  
Jaxk H. Reeves ◽  
Ashim Mallik ◽  
William P. McCormick
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
High Efficiency ◽  
Asymptotic Properties ◽  
Likelihood Estimation ◽  
Small Sample ◽  
Sequential Procedure ◽  
Pricing Scheme ◽  
Simulation Results ◽  
Small Sample Sizes

A sequential procedure to select optimal prices based on maximum likelihood estimation is considered. Asymptotic properties of the pricing scheme and the concommitant estimation problem are examined. For small sample sizes, simulation results show that the proposed procedure has high efficiency relative to the best procedure when the parameter is known.

scholarly journals "Evaluation of Some Methods for Estimating the Weibull Distribution Parameters"

2017 ◽  
Vol 4 (2) ◽  
pp. 8-14
Author(s):  
J. A. Labban ◽  
H. H. Depheal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Least Squares ◽  
Mean Square Error ◽  
Random Sample ◽  
Likelihood Estimation ◽  
Mean Square ◽  
Distribution Parameters

"This paper some of different methods to estimate the parameters of the 2-Paramaters Weibull distribution such as (Maximum likelihood Estimation, Moments, Least Squares, Term Omission). Mean square error will be considered to compare methods fits in case to select the best one. There by simulation will be implemented to generate different random sample of the 2-parameters Weibull distribution, those contain (n=10, 50, 100, 200) iteration each 1000 times."

scholarly journals A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1394
Author(s):  
Mustapha Muhammad ◽  
Huda M. Alshanbari ◽  
Ayed R. A. Alanzi ◽  
Lixia Liu ◽  
Waqas Sami ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Confidence Intervals ◽  
Likelihood Estimation ◽  
Quantile Function ◽  
Weibull Model ◽  
Mean Square ◽  
Reliability Parameter ◽  
Coverage Probabilities ◽  
The Mean

In this article, we propose the exponentiated sine-generated family of distributions. Some important properties are demonstrated, such as the series representation of the probability density function, quantile function, moments, stress-strength reliability, and Rényi entropy. A particular member, called the exponentiated sine Weibull distribution, is highlighted; we analyze its skewness and kurtosis, moments, quantile function, residual mean and reversed mean residual life functions, order statistics, and extreme value distributions. Maximum likelihood estimation and Bayes estimation under the square error loss function are considered. Simulation studies are used to assess the techniques, and their performance gives satisfactory results as discussed by the mean square error, confidence intervals, and coverage probabilities of the estimates. The stress-strength reliability parameter of the exponentiated sine Weibull model is derived and estimated by the maximum likelihood estimation method. Also, nonparametric bootstrap techniques are used to approximate the confidence interval of the reliability parameter. A simulation is conducted to examine the mean square error, standard deviations, confidence intervals, and coverage probabilities of the reliability parameter. Finally, three real applications of the exponentiated sine Weibull model are provided. One of them considers stress-strength data.

scholarly journals Quantifying the strength of general factors in psychopathology: A comparison of CFA with maximum likelihood estimation, BSEM and ESEM/EFA bi-factor approaches

2018 ◽  
Author(s):  
Aja Louise Murray ◽  
Tom Booth ◽  
Manuel Eisner ◽  
Ingrid Obsuth ◽  
Denis Ribeaud
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Factor Models ◽  
General Factor ◽  
Normative Sample ◽  
Ml Estimation ◽  
Co Morbidity ◽  
Simulation Results ◽  
P Factor

Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p-factor) in classifying, researching, diagnosing and treating psychiatric disorders depends (amongst other issues) on the extent to which co-morbidity is symptom-general rather than staying largely within the confines of narrower trans-diagnostic factors such as internalising and externalising. In this study we compared three methods of estimating p-factor strength. We compared omega hierarchical and ECV calculated from CFA bi-factor models with maximum likelihood (ML) estimation, from ESEM/EFA models with a bifactor rotation, and from BSEM bi-factor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings may be the preferred option. However, CFA with ML also performed well provided secondary loadings were modelled We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n=1286) and University counselling sample (n= 359).

New Testifying Method of Unit Weight Mean Square Error of No-Equal Precision Independent Surveying Values

2012 ◽  
Vol 170-173 ◽  
pp. 2904-2907 ◽  
Author(s):  
Yong He Deng
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Mean Square Error ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
New Method ◽  
Unit Weight ◽  
Mean Square ◽  
Maximum Likelihood Estimation Method

For unit weight mean square error of no-equal precision independent surveying values,this paper summed up several old estifying methods, pointed out their scarcities or mistakes, and advanced a sort of new method- maximum likelihood estimation method which is simple and strict.This is useful for theory of unit weight mean square error of no-equal precision independent surveying values to be perfect and for college surveying textbook to be improved and unified.

An Image Restoration Method for Earthquake Ruin Scene

2013 ◽  
Vol 321-324 ◽  
pp. 1107-1110
Author(s):  
Yan Xing Song ◽  
Jing Song Yang ◽  
Qin Gao ◽  
Shu Cong Liu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Image Restoration ◽  
Estimation Method ◽  
Operation Time ◽  
Likelihood Estimation ◽  
Simulation Results ◽  
Restoration Method ◽  
Maximum Likelihood Estimation Method

An image restoration method for earthquake ruin scene is proposed in this paper. This method uses maximum likelihood estimation method based on Poisson (PMLE). This algorithm is introduced at first, then an image of earthquake ruin scene is processed with this image restoration method. Simulation results show that iterations in this method will effect the PSNR of the processed image and operation time, and this method can restore image of earthquake ruin scene effectively and has a good practicability.

scholarly journals Bayes Estimators for the Parameter of the Inverted Exponential Distribution Under different Double informative priors

2018 ◽  
Vol 24 (103) ◽  
pp. 18
Author(s):  
جنان عباس ناصر
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Exponential Distribution ◽  
Likelihood Estimation ◽  
Bayes Estimation ◽  
Erlang Distribution ◽  
Mean Square ◽  
Informative Priors ◽  
Chi Squared ◽  
Mean Square Errors

In this paper, we present a comparison of double informative priors which are assumed for the parameter of inverted exponential distribution.To estimate the parameter of inverted exponential distribution by using Bayes estimation ,will be  used two different kind of information in the Bayes estimation; two different priors have been selected for the parameter of inverted exponential distribution. Also assumed Chi-squared - Gamma distribution, Chi-squared - Erlang distribution, and- Gamma- Erlang distribution as double priors. The results are the derivations of these estimators under the squared error loss function with three different double priors. Additionally Maximum likelihood estimation method (MLE) was used  to estimate the parameter of inverted exponential distribution .We used simulation technique, to compare the performance for each estimator, several cases from inverted exponential distribution for data generating, for different samples sizes (small, medium, and large).Simulation results shown that the best method is the bayes  estimation according to the smallest values of mean square errors( MSE) for all samples sizes (n) comparative to the estimated values by using MLE . According to obtained results, we see that when the double prior distribution for  is Gamma- Erlang distribution for some values for the parameters a, b & given the best results according to the smallest values of mean square errors (MSE) comparative to the same values which obtained by using Maximum likelihood estimation (MLE) for the assuming true values for and for all samples sizes.  

scholarly journals Item-Weighted Likelihood Method for Measuring Growth in Longitudinal Study With Tests Composed of Both Dichotomous and Polytomous Items

2021 ◽  
Vol 12 ◽  
Author(s):  
Xuemei Xue ◽  
Jing Lu ◽  
Jiwei Zhang
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Mean Square ◽  
Map Estimation ◽  
Weighted Likelihood ◽  
Irt Models ◽  
Weighted Likelihood Estimation ◽  
New Item

In this paper, a new item-weighted scheme is proposed to assess examinees’ growth in longitudinal analysis. A multidimensional Rasch model for measuring learning and change (MRMLC) and its polytomous extension is used to fit the longitudinal item response data. In fact, the new item-weighted likelihood estimation method is not only suitable for complex longitudinal IRT models, but also it can be used to estimate the unidimensional IRT models. For example, the combination of the two-parameter logistic (2PL) model and the partial credit model (PCM, Masters, 1982) with a varying number of categories. Two simulation studies are carried out to further illustrate the advantages of the item-weighted likelihood estimation method compared to the traditional Maximum a Posteriori (MAP) estimation method, Maximum likelihood estimation method (MLE), Warm’s (1989) weighted likelihood estimation (WLE) method, and type-weighted maximum likelihood estimation (TWLE) method. Simulation results indicate that the improved item-weighted likelihood estimation method better recover examinees’ true ability level for both complex longitudinal IRT models and unidimensional IRT models compared to the existing likelihood estimation (MLE, WLE and TWLE) methods and MAP estimation method, with smaller bias, root-mean-square errors, and root-mean-square difference especially at the low-and high-ability levels.

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